Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations6172
Missing cells461
Missing cells (%)0.6%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory2.4 MiB
Average record size in memory414.0 B

Variable types

Text2
Categorical2
DateTime1
Numeric8

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
EU_Sales is highly overall correlated with Global_Sales and 2 other fieldsHigh correlation
Global_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
NA_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
Other_Sales is highly overall correlated with EU_Sales and 2 other fieldsHigh correlation
user_review has 357 (5.8%) missing values Missing
Year has 103 (1.7%) missing values Missing
NA_Sales has 501 (8.1%) zeros Zeros
EU_Sales has 773 (12.5%) zeros Zeros
JP_Sales has 4287 (69.5%) zeros Zeros
Other_Sales has 1198 (19.4%) zeros Zeros

Reproduction

Analysis started2025-03-12 18:44:49.346683
Analysis finished2025-03-12 18:44:53.641031
Duration4.29 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Name
Text

Distinct4027
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size468.4 KiB
2025-03-12T20:44:53.761440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length66
Median length48
Mean length20.698801
Min length2

Characters and Unicode

Total characters127753
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2762 ?
Unique (%)44.8%

Sample

1st rowThe Legend of Zelda: Ocarina of Time
2nd rowTony Hawk's Pro Skater 2
3rd rowGrand Theft Auto IV
4th rowSoulCalibur
5th rowGrand Theft Auto IV
ValueCountFrequency (%)
the 1124
 
5.2%
of 841
 
3.9%
2 502
 
2.3%
3 262
 
1.2%
147
 
0.7%
ii 139
 
0.6%
world 121
 
0.6%
nba 115
 
0.5%
wars 110
 
0.5%
nfl 104
 
0.5%
Other values (3661) 18286
84.1%
2025-03-12T20:44:53.971032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15579
 
12.2%
e 10513
 
8.2%
a 8138
 
6.4%
o 7516
 
5.9%
r 7212
 
5.6%
n 6382
 
5.0%
i 6226
 
4.9%
t 5724
 
4.5%
s 4926
 
3.9%
l 4296
 
3.4%
Other values (67) 51241
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15579
 
12.2%
e 10513
 
8.2%
a 8138
 
6.4%
o 7516
 
5.9%
r 7212
 
5.6%
n 6382
 
5.0%
i 6226
 
4.9%
t 5724
 
4.5%
s 4926
 
3.9%
l 4296
 
3.4%
Other values (67) 51241
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15579
 
12.2%
e 10513
 
8.2%
a 8138
 
6.4%
o 7516
 
5.9%
r 7212
 
5.6%
n 6382
 
5.0%
i 6226
 
4.9%
t 5724
 
4.5%
s 4926
 
3.9%
l 4296
 
3.4%
Other values (67) 51241
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15579
 
12.2%
e 10513
 
8.2%
a 8138
 
6.4%
o 7516
 
5.9%
r 7212
 
5.6%
n 6382
 
5.0%
i 6226
 
4.9%
t 5724
 
4.5%
s 4926
 
3.9%
l 4296
 
3.4%
Other values (67) 51241
40.1%

Platform
Categorical

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size391.6 KiB
PlayStation 2
1042 
Xbox 360
714 
PlayStation 3
644 
PC
608 
Xbox
583 
Other values (13)
2581 

Length

Max length16
Median length11
Mean length7.9460467
Min length2

Characters and Unicode

Total characters49043
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNintendo 64
2nd rowPlayStation
3rd rowPlayStation 3
4th rowDreamcast
5th rowXbox 360

Common Values

ValueCountFrequency (%)
PlayStation 2 1042
16.9%
Xbox 360 714
11.6%
PlayStation 3 644
10.4%
PC 608
9.9%
Xbox 583
9.4%
DS 474
7.7%
Wii 384
 
6.2%
GameCube 352
 
5.7%
PSP 343
 
5.6%
Game Boy Advance 292
 
4.7%
Other values (8) 736
11.9%

Length

2025-03-12T20:44:54.063693image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
playstation 2076
21.6%
xbox 1400
14.6%
2 1042
10.8%
360 714
 
7.4%
3 644
 
6.7%
pc 608
 
6.3%
ds 474
 
4.9%
wii 438
 
4.6%
gamecube 352
 
3.7%
psp 343
 
3.6%
Other values (11) 1530
15.9%

Most occurring characters

ValueCountFrequency (%)
a 5205
 
10.6%
t 4311
 
8.8%
o 3823
 
7.8%
3449
 
7.0%
P 3370
 
6.9%
i 3098
 
6.3%
S 3014
 
6.1%
n 2581
 
5.3%
y 2368
 
4.8%
l 2076
 
4.2%
Other values (26) 15748
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5205
 
10.6%
t 4311
 
8.8%
o 3823
 
7.8%
3449
 
7.0%
P 3370
 
6.9%
i 3098
 
6.3%
S 3014
 
6.1%
n 2581
 
5.3%
y 2368
 
4.8%
l 2076
 
4.2%
Other values (26) 15748
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5205
 
10.6%
t 4311
 
8.8%
o 3823
 
7.8%
3449
 
7.0%
P 3370
 
6.9%
i 3098
 
6.3%
S 3014
 
6.1%
n 2581
 
5.3%
y 2368
 
4.8%
l 2076
 
4.2%
Other values (26) 15748
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5205
 
10.6%
t 4311
 
8.8%
o 3823
 
7.8%
3449
 
7.0%
P 3370
 
6.9%
i 3098
 
6.3%
S 3014
 
6.1%
n 2581
 
5.3%
y 2368
 
4.8%
l 2076
 
4.2%
Other values (26) 15748
32.1%
Distinct1944
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size48.3 KiB
Minimum1996-03-30 00:00:00
Maximum2020-08-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-12T20:44:54.128247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:54.196178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

meta_score
Real number (ℝ)

Distinct78
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.974401
Minimum22
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:54.265207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile57
Q166
median74
Q381
95-th percentile90
Maximum99
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.691893
Coefficient of variation (CV)0.16021911
Kurtosis1.7530867
Mean72.974401
Median Absolute Deviation (MAD)8
Skewness-0.87312041
Sum450398
Variance136.70037
MonotonicityDecreasing
2025-03-12T20:44:54.340335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 224
 
3.6%
70 223
 
3.6%
78 222
 
3.6%
75 218
 
3.5%
71 214
 
3.5%
76 211
 
3.4%
74 210
 
3.4%
73 206
 
3.3%
77 203
 
3.3%
81 203
 
3.3%
Other values (68) 4038
65.4%
ValueCountFrequency (%)
22 2
 
< 0.1%
23 3
< 0.1%
24 3
< 0.1%
25 4
0.1%
26 7
0.1%
27 4
0.1%
28 6
0.1%
29 3
< 0.1%
30 4
0.1%
31 3
< 0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
98 4
 
0.1%
97 12
 
0.2%
96 19
 
0.3%
95 17
 
0.3%
94 36
0.6%
93 42
0.7%
92 56
0.9%
91 74
1.2%
90 83
1.3%

user_review
Real number (ℝ)

Missing 

Distinct10
Distinct (%)0.2%
Missing357
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean7.372485
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:54.405586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median8
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2614251
Coefficient of variation (CV)0.17109904
Kurtosis2.4556693
Mean7.372485
Median Absolute Deviation (MAD)1
Skewness-1.3666
Sum42871
Variance1.5911933
MonotonicityNot monotonic
2025-03-12T20:44:54.469749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 2645
42.9%
7 1305
21.1%
9 722
 
11.7%
6 703
 
11.4%
5 206
 
3.3%
4 146
 
2.4%
3 55
 
0.9%
2 30
 
0.5%
10 2
 
< 0.1%
1 1
 
< 0.1%
(Missing) 357
 
5.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 30
 
0.5%
3 55
 
0.9%
4 146
 
2.4%
5 206
 
3.3%
6 703
 
11.4%
7 1305
21.1%
8 2645
42.9%
9 722
 
11.7%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 722
 
11.7%
8 2645
42.9%
7 1305
21.1%
6 703
 
11.4%
5 206
 
3.3%
4 146
 
2.4%
3 55
 
0.9%
2 30
 
0.5%
1 1
 
< 0.1%

Year
Real number (ℝ)

Missing 

Distinct22
Distinct (%)0.4%
Missing103
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean2006.9366
Minimum1988
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:54.527824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2001
Q12004
median2007
Q32010
95-th percentile2014
Maximum2016
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1848476
Coefficient of variation (CV)0.0020851918
Kurtosis-0.57847808
Mean2006.9366
Median Absolute Deviation (MAD)3
Skewness0.16135698
Sum12180098
Variance17.512949
MonotonicityNot monotonic
2025-03-12T20:44:54.588417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2007 527
 
8.5%
2005 519
 
8.4%
2008 498
 
8.1%
2003 488
 
7.9%
2002 477
 
7.7%
2009 471
 
7.6%
2006 462
 
7.5%
2004 451
 
7.3%
2010 374
 
6.1%
2011 355
 
5.8%
Other values (12) 1447
23.4%
ValueCountFrequency (%)
1988 1
 
< 0.1%
1996 12
 
0.2%
1997 27
 
0.4%
1998 28
 
0.5%
1999 39
 
0.6%
2000 123
 
2.0%
2001 270
4.4%
2002 477
7.7%
2003 488
7.9%
2004 451
7.3%
ValueCountFrequency (%)
2016 107
 
1.7%
2015 180
 
2.9%
2014 189
 
3.1%
2013 208
 
3.4%
2012 263
4.3%
2011 355
5.8%
2010 374
6.1%
2009 471
7.6%
2008 498
8.1%
2007 527
8.5%

Genre
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size387.2 KiB
Action
1351 
Sports
930 
Shooter
721 
Role-Playing
639 
Racing
535 
Other values (7)
1996 

Length

Max length12
Median length10
Mean length7.2185677
Min length4

Characters and Unicode

Total characters44553
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAction
2nd rowSports
3rd rowAction
4th rowFighting
5th rowAction

Common Values

ValueCountFrequency (%)
Action 1351
21.9%
Sports 930
15.1%
Shooter 721
11.7%
Role-Playing 639
10.4%
Racing 535
 
8.7%
Platform 385
 
6.2%
Misc 361
 
5.8%
Fighting 319
 
5.2%
Strategy 275
 
4.5%
Simulation 266
 
4.3%
Other values (2) 390
 
6.3%

Length

2025-03-12T20:44:54.648264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action 1351
21.9%
sports 930
15.1%
shooter 721
11.7%
role-playing 639
10.4%
racing 535
 
8.7%
platform 385
 
6.2%
misc 361
 
5.8%
fighting 319
 
5.2%
strategy 275
 
4.5%
simulation 266
 
4.3%
Other values (2) 390
 
6.3%

Most occurring characters

ValueCountFrequency (%)
o 5013
 
11.3%
t 4744
 
10.6%
i 4056
 
9.1%
n 3332
 
7.5%
r 2533
 
5.7%
e 2247
 
5.0%
c 2247
 
5.0%
S 2192
 
4.9%
a 2100
 
4.7%
l 2097
 
4.7%
Other values (17) 13992
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5013
 
11.3%
t 4744
 
10.6%
i 4056
 
9.1%
n 3332
 
7.5%
r 2533
 
5.7%
e 2247
 
5.0%
c 2247
 
5.0%
S 2192
 
4.9%
a 2100
 
4.7%
l 2097
 
4.7%
Other values (17) 13992
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5013
 
11.3%
t 4744
 
10.6%
i 4056
 
9.1%
n 3332
 
7.5%
r 2533
 
5.7%
e 2247
 
5.0%
c 2247
 
5.0%
S 2192
 
4.9%
a 2100
 
4.7%
l 2097
 
4.7%
Other values (17) 13992
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5013
 
11.3%
t 4744
 
10.6%
i 4056
 
9.1%
n 3332
 
7.5%
r 2533
 
5.7%
e 2247
 
5.0%
c 2247
 
5.0%
S 2192
 
4.9%
a 2100
 
4.7%
l 2097
 
4.7%
Other values (17) 13992
31.4%
Distinct244
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Memory size426.6 KiB
2025-03-12T20:44:54.773387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length38
Median length27
Mean length13.761951
Min length3

Characters and Unicode

Total characters84925
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)1.4%

Sample

1st rowNintendo
2nd rowActivision
3rd rowTake-Two Interactive
4th rowNamco Bandai Games
5th rowTake-Two Interactive
ValueCountFrequency (%)
entertainment 867
 
7.8%
electronic 836
 
7.5%
arts 836
 
7.5%
interactive 681
 
6.1%
games 614
 
5.5%
activision 409
 
3.7%
ubisoft 391
 
3.5%
sony 330
 
3.0%
computer 323
 
2.9%
nintendo 315
 
2.8%
Other values (291) 5580
49.9%
2025-03-12T20:44:55.152412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 8541
 
10.1%
e 7553
 
8.9%
i 7372
 
8.7%
n 6986
 
8.2%
a 5527
 
6.5%
o 5408
 
6.4%
r 5048
 
5.9%
5011
 
5.9%
c 3892
 
4.6%
s 3663
 
4.3%
Other values (53) 25924
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 8541
 
10.1%
e 7553
 
8.9%
i 7372
 
8.7%
n 6986
 
8.2%
a 5527
 
6.5%
o 5408
 
6.4%
r 5048
 
5.9%
5011
 
5.9%
c 3892
 
4.6%
s 3663
 
4.3%
Other values (53) 25924
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 8541
 
10.1%
e 7553
 
8.9%
i 7372
 
8.7%
n 6986
 
8.2%
a 5527
 
6.5%
o 5408
 
6.4%
r 5048
 
5.9%
5011
 
5.9%
c 3892
 
4.6%
s 3663
 
4.3%
Other values (53) 25924
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 8541
 
10.1%
e 7553
 
8.9%
i 7372
 
8.7%
n 6986
 
8.2%
a 5527
 
6.5%
o 5408
 
6.4%
r 5048
 
5.9%
5011
 
5.9%
c 3892
 
4.6%
s 3663
 
4.3%
Other values (53) 25924
30.5%

NA_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct361
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41823882
Minimum0
Maximum41.49
Zeros501
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:55.234161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.06
median0.15
Q30.42
95-th percentile1.6445
Maximum41.49
Range41.49
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation1.0240816
Coefficient of variation (CV)2.448557
Kurtosis467.30319
Mean0.41823882
Median Absolute Deviation (MAD)0.12
Skewness15.28473
Sum2581.37
Variance1.0487431
MonotonicityNot monotonic
2025-03-12T20:44:55.305164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 501
 
8.1%
0.05 211
 
3.4%
0.01 204
 
3.3%
0.03 202
 
3.3%
0.02 198
 
3.2%
0.09 194
 
3.1%
0.06 188
 
3.0%
0.11 184
 
3.0%
0.07 184
 
3.0%
0.04 178
 
2.9%
Other values (351) 3928
63.6%
ValueCountFrequency (%)
0 501
8.1%
0.01 204
3.3%
0.02 198
 
3.2%
0.03 202
3.3%
0.04 178
 
2.9%
0.05 211
3.4%
0.06 188
 
3.0%
0.07 184
 
3.0%
0.08 176
 
2.9%
0.09 194
 
3.1%
ValueCountFrequency (%)
41.49 1
< 0.1%
15.85 1
< 0.1%
15.75 1
< 0.1%
14.97 1
< 0.1%
14.59 1
< 0.1%
14.03 1
< 0.1%
11.38 1
< 0.1%
9.81 1
< 0.1%
9.67 1
< 0.1%
9.63 1
< 0.1%

EU_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct271
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24947181
Minimum0
Maximum29.02
Zeros773
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:55.374388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.06
Q30.22
95-th percentile1.06
Maximum29.02
Range29.02
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.71297804
Coefficient of variation (CV)2.8579504
Kurtosis482.90039
Mean0.24947181
Median Absolute Deviation (MAD)0.06
Skewness15.729898
Sum1539.74
Variance0.50833769
MonotonicityNot monotonic
2025-03-12T20:44:55.440033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 773
 
12.5%
0.01 609
 
9.9%
0.02 560
 
9.1%
0.03 446
 
7.2%
0.04 329
 
5.3%
0.05 242
 
3.9%
0.06 188
 
3.0%
0.07 171
 
2.8%
0.08 155
 
2.5%
0.1 127
 
2.1%
Other values (261) 2572
41.7%
ValueCountFrequency (%)
0 773
12.5%
0.01 609
9.9%
0.02 560
9.1%
0.03 446
7.2%
0.04 329
5.3%
0.05 242
 
3.9%
0.06 188
 
3.0%
0.07 171
 
2.8%
0.08 155
 
2.5%
0.09 121
 
2.0%
ValueCountFrequency (%)
29.02 1
< 0.1%
12.88 1
< 0.1%
11.01 1
< 0.1%
9.27 1
< 0.1%
9.23 1
< 0.1%
9.2 1
< 0.1%
8.59 1
< 0.1%
8.03 1
< 0.1%
7.57 1
< 0.1%
7.06 1
< 0.1%

JP_Sales
Real number (ℝ)

Zeros 

Distinct160
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071088788
Minimum0
Maximum6.5
Zeros4287
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:55.645681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.33
Maximum6.5
Range6.5
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.30051247
Coefficient of variation (CV)4.2272836
Kurtosis131.11076
Mean0.071088788
Median Absolute Deviation (MAD)0
Skewness9.8197996
Sum438.76
Variance0.090307743
MonotonicityNot monotonic
2025-03-12T20:44:55.727894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4287
69.5%
0.01 282
 
4.6%
0.02 183
 
3.0%
0.03 146
 
2.4%
0.04 118
 
1.9%
0.06 88
 
1.4%
0.05 83
 
1.3%
0.08 68
 
1.1%
0.07 59
 
1.0%
0.1 49
 
0.8%
Other values (150) 809
 
13.1%
ValueCountFrequency (%)
0 4287
69.5%
0.01 282
 
4.6%
0.02 183
 
3.0%
0.03 146
 
2.4%
0.04 118
 
1.9%
0.05 83
 
1.3%
0.06 88
 
1.4%
0.07 59
 
1.0%
0.08 68
 
1.1%
0.09 37
 
0.6%
ValueCountFrequency (%)
6.5 1
< 0.1%
5.33 1
< 0.1%
5.32 1
< 0.1%
4.7 1
< 0.1%
4.36 1
< 0.1%
4.35 1
< 0.1%
4.13 2
< 0.1%
3.79 1
< 0.1%
3.77 1
< 0.1%
3.63 1
< 0.1%

Other_Sales
Real number (ℝ)

High correlation  Zeros 

Distinct143
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085706416
Minimum0
Maximum10.57
Zeros1198
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:55.801013image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.07
95-th percentile0.32
Maximum10.57
Range10.57
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.28182026
Coefficient of variation (CV)3.2882049
Kurtosis537.91455
Mean0.085706416
Median Absolute Deviation (MAD)0.02
Skewness18.476405
Sum528.98
Variance0.079422658
MonotonicityNot monotonic
2025-03-12T20:44:55.868418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 1356
22.0%
0 1198
19.4%
0.02 673
10.9%
0.03 432
 
7.0%
0.04 331
 
5.4%
0.05 255
 
4.1%
0.06 229
 
3.7%
0.07 182
 
2.9%
0.08 138
 
2.2%
0.09 130
 
2.1%
Other values (133) 1248
20.2%
ValueCountFrequency (%)
0 1198
19.4%
0.01 1356
22.0%
0.02 673
10.9%
0.03 432
 
7.0%
0.04 331
 
5.4%
0.05 255
 
4.1%
0.06 229
 
3.7%
0.07 182
 
2.9%
0.08 138
 
2.2%
0.09 130
 
2.1%
ValueCountFrequency (%)
10.57 1
< 0.1%
8.46 1
< 0.1%
7.53 1
< 0.1%
4.14 1
< 0.1%
3.31 1
< 0.1%
2.96 1
< 0.1%
2.93 1
< 0.1%
2.9 1
< 0.1%
2.85 1
< 0.1%
2.52 1
< 0.1%

Global_Sales
Real number (ℝ)

High correlation 

Distinct539
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8247197
Minimum0.01
Maximum82.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.3 KiB
2025-03-12T20:44:55.932403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.11
median0.3
Q30.81
95-th percentile3.1345
Maximum82.74
Range82.73
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.060152
Coefficient of variation (CV)2.4980027
Kurtosis461.6098
Mean0.8247197
Median Absolute Deviation (MAD)0.24
Skewness15.424586
Sum5090.17
Variance4.2442263
MonotonicityNot monotonic
2025-03-12T20:44:55.994884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 206
 
3.3%
0.05 184
 
3.0%
0.03 173
 
2.8%
0.04 157
 
2.5%
0.06 143
 
2.3%
0.08 140
 
2.3%
0.07 133
 
2.2%
0.09 128
 
2.1%
0.11 123
 
2.0%
0.1 123
 
2.0%
Other values (529) 4662
75.5%
ValueCountFrequency (%)
0.01 86
1.4%
0.02 206
3.3%
0.03 173
2.8%
0.04 157
2.5%
0.05 184
3.0%
0.06 143
2.3%
0.07 133
2.2%
0.08 140
2.3%
0.09 128
2.1%
0.1 123
2.0%
ValueCountFrequency (%)
82.74 1
< 0.1%
35.82 1
< 0.1%
33 1
< 0.1%
30.01 1
< 0.1%
29.02 1
< 0.1%
28.62 1
< 0.1%
23.42 1
< 0.1%
22.72 1
< 0.1%
22 1
< 0.1%
21.82 1
< 0.1%

Interactions

2025-03-12T20:44:53.098229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.110790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.577915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.911528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.275883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.774581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.314093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.655444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.148428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.197691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.623217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.960675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.338097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.822168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.362969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.701396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.187856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.273359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.662083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.006219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.392812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.862846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.406957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.740128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.231637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.339794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.702965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.050813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.574912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.905658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.449370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.780853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.270476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.399986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.742361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.095509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.612672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.950756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.488534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.820625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.312702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.445556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.788467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.141228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.653799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.998986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.532106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.861970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.356614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.491932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.831542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.187869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.694912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.211451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.574126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.904337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.399557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.534827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:50.872121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.231605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:51.736047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.265345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:52.615390image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-12T20:44:53.030197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-12T20:44:56.039819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
EU_SalesGenreGlobal_SalesJP_SalesNA_SalesOther_SalesPlatformYearmeta_scoreuser_review
EU_Sales1.0000.0000.7980.2770.6370.7930.0360.0920.3660.137
Genre0.0001.0000.0270.0420.0390.0000.1570.0910.0720.060
Global_Sales0.7980.0271.0000.4390.9090.8890.0390.0730.4020.173
JP_Sales0.2770.0420.4391.0000.2910.3490.0830.1580.2220.208
NA_Sales0.6370.0390.9090.2911.0000.7730.054-0.0210.3400.146
Other_Sales0.7930.0000.8890.3490.7731.0000.0120.2200.3340.106
Platform0.0360.1570.0390.0830.0540.0121.0000.4910.0900.107
Year0.0920.0910.0730.158-0.0210.2200.4911.000-0.004-0.261
meta_score0.3660.0720.4020.2220.3400.3340.090-0.0041.0000.464
user_review0.1370.0600.1730.2080.1460.1060.107-0.2610.4641.000

Missing values

2025-03-12T20:44:53.461760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T20:44:53.546546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-12T20:44:53.607968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NamePlatformrelease_datemeta_scoreuser_reviewYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales
0The Legend of Zelda: Ocarina of TimeNintendo 64November 23, 1998999.01998.0ActionNintendo4.101.891.450.167.60
1Tony Hawk's Pro Skater 2PlayStationSeptember 20, 2000987.02000.0SportsActivision3.051.410.020.204.68
2Grand Theft Auto IVPlayStation 3April 29, 2008988.02008.0ActionTake-Two Interactive4.763.760.441.6210.57
3SoulCaliburDreamcastSeptember 8, 1999988.01999.0FightingNamco Bandai Games0.000.000.340.000.34
4Grand Theft Auto IVXbox 360April 29, 2008988.02008.0ActionTake-Two Interactive6.763.100.141.0311.02
5Super Mario GalaxyWiiNovember 12, 2007979.02007.0PlatformNintendo6.163.401.200.7611.52
6Super Mario Galaxy 2WiiMay 23, 2010979.02010.0PlatformNintendo3.662.420.980.647.69
7Grand Theft Auto VXbox OneNovember 18, 2014978.02014.0ActionTake-Two Interactive2.662.010.000.415.08
8Grand Theft Auto VPlayStation 3September 17, 2013978.02013.0ActionTake-Two Interactive7.019.270.974.1421.40
9Grand Theft Auto VXbox 360September 17, 2013978.02013.0ActionTake-Two Interactive9.635.310.061.3816.38
NamePlatformrelease_datemeta_scoreuser_reviewYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales
6162Batman: Dark TomorrowXboxMarch 18, 2003253.02003.0ActionKemco0.080.020.00.000.11
6163Cruis'nWiiNovember 27, 2007254.02007.0RacingMidway Games0.100.010.00.010.12
6164Gravity Games Bike: Street Vert DirtPlayStation 2June 27, 2002243.02002.0SportsMidway Games0.050.040.00.010.10
6165Legends of Wrestling IIGame Boy AdvanceNovember 25, 2002244.02002.0FightingAcclaim Entertainment0.050.020.00.000.07
6166Pulse RacerXboxJanuary 2, 2003242.02003.0RacingJaleco0.010.000.00.000.02
6167Fighter WithinXbox OneNovember 19, 2013233.02013.0FightingUbisoft0.110.070.00.020.19
6168Homie RollerzDSMarch 5, 2008233.02008.0RacingDestineer0.070.000.00.010.07
6169Charlie's AngelsGameCubeJuly 9, 2003234.02003.0ActionUbisoft0.010.000.00.000.02
6170Fast & Furious: ShowdownXbox 360May 21, 2013221.02013.0ActionActivision0.000.040.00.000.05
6171Drake of the 99 DragonsXboxNovember 3, 2003222.0NaNShooterUnknown0.050.010.00.000.07

Duplicate rows

Most frequently occurring

NamePlatformrelease_datemeta_scoreuser_reviewYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales# duplicates
0Naughty BearXbox 360June 25, 2010436.02010.0Action505 Games0.210.160.00.040.412